论文标题
使用统计仿真和粒状面扩散的贝叶斯推断反应速率参数的推断：甘氨酸网络的应用
Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network
论文作者
论文摘要
围绕星际晶粒表面化学的不确定性存在许多不确定性。主要反应机制之一是晶表扩散，每个物种的结合能参数都需要知道。但是，这些值在文献中有显着差异，这可能会导致关于是否通过扩散发生特定反应的争论。在这项工作中，我们采用贝叶斯推断利用可用的冰丰度来估计产生甘氨酸的化学网络中反应的反应速率。使用此反应是通过扩散发生的，我们可以通过此估计网络中各种重要物种的结合能。我们利用对扩散机制的理解，通过证明可以将反应分为类别，将推理问题的维度从49降低到14。降低维度使问题在计算上可行。神经网络统计模拟器还用于有助于大大加速贝叶斯推理过程。 发现大多数扩散物种的结合能与某些不同的文献值相匹配，但原子和双原子氢除外。这两个物种的差异与物理和化学模型的局限性有关。然而，发现形式H + x-> hx的虚拟反应的使用可以在某种程度上减少与原子氢的结合能的差异。在UCLCHEM的完整气体版本中，使用推断的结合能导致几乎所有分子丰度正在回收。
There exists much uncertainty surrounding interstellar grain-surface chemistry. One of the major reaction mechanisms is grain-surface diffusion for which the the binding energy parameter for each species needs to be known. However, these values vary significantly across the literature which can lead to debate as to whether or not a particular reaction takes place via diffusion. In this work we employ Bayesian inference to use available ice abundances to estimate the reaction rates of the reactions in a chemical network that produces glycine. Using this we estimate the binding energy of a variety of important species in the network, by assuming that the reactions take place via diffusion. We use our understanding of the diffusion mechanism to reduce the dimensionality of the inference problem from 49 to 14, by demonstrating that reactions can be separated into classes. This dimensionality reduction makes the problem computationally feasible. A neural network statistical emulator is used to also help accelerate the Bayesian inference process substantially. The binding energies of most of the diffusive species of interest are found to match some of the disparate literature values, with the exceptions of atomic and diatomic hydrogen. The discrepancies with these two species are related to limitations of the physical and chemical model. However, the use of a dummy reaction of the form H + X -> HX is found to somewhat reduce the discrepancy with the binding energy of atomic hydrogen. Using the inferred binding energies in the full gas-grain version of UCLCHEM results in almost all the molecular abundances being recovered.
